Update app.py

app.py

@@ -131,7 +131,7 @@ class DicomAnalyzer:
             clicked_x = evt.index[0]
             clicked_y = evt.index[1]
             
-            # Transform coordinates
+            # Transform coordinates to match ImageJ exactly
             x = clicked_x + self.pan_x
             y = clicked_y + self.pan_y
             if self.zoom_factor != 1.0:
@@ -144,22 +144,19 @@ class DicomAnalyzer:
             # Get image dimensions
             height, width = self.original_image.shape[:2]
             
-            # Create mask with larger radius calculation
+            # Create mask using exact ImageJ method
             Y, X = np.ogrid[:height, :width]
             
-            # Increase effective radius to include more boundary pixels
-            radius = (self.circle_diameter / 2.0) + 1.0  # Increased from 0.5 to 1.0
-            r_squared = radius * radius
+            # Use exact 9-pixel diameter
+            radius = self.circle_diameter / 2.0
             
-            # Calculate distances with more inclusive boundary
+            # Calculate distances using ImageJ's method
             dx = X - x
             dy = Y - y
             dist_squared = dx*dx + dy*dy
+            mask = dist_squared <= (radius * radius)
             
-            # Include more pixels in the mask
-            mask = dist_squared <= (r_squared + 0.5)  # Increased tolerance
-            
-            # Get ROI pixels
+            # Get ROI pixels from original image
             roi_pixels = self.original_image[mask]
             
             if len(roi_pixels) == 0:
@@ -168,17 +165,19 @@ class DicomAnalyzer:
             # Get pixel spacing (mm/pixel)
             pixel_spacing = float(self.dicom_data.PixelSpacing[0])
             
-            # Calculate statistics
+            # Calculate area using exact pixel count
             n_pixels = np.sum(mask)
             area = n_pixels * (pixel_spacing ** 2)
+            
+            # Calculate statistics
             mean_value = np.mean(roi_pixels)
-            std_dev = np.std(roi_pixels, ddof=1)
+            std_dev = np.std(roi_pixels, ddof=1)  # ImageJ uses n-1
             min_val = np.min(roi_pixels)
             max_val = np.max(roi_pixels)
 
-            print(f"\nEnhanced Analysis:")
+            print(f"\nImageJ-compatible Analysis:")
             print(f"Position: ({x}, {y})")
-            print(f"Effective Radius: {radius}")
+            print(f"Diameter: {self.circle_diameter} pixels")
             print(f"Pixel count: {n_pixels}")
             print(f"Area: {area:.3f} mm²")
             print(f"Mean: {mean_value:.3f}")
@@ -222,8 +221,8 @@ class DicomAnalyzer:
             for x, y, diameter in self.marks:
                 zoomed_x = int(x * self.zoom_factor)
                 zoomed_y = int(y * self.zoom_factor)
-                # Use enhanced radius for display
-                zoomed_radius = int(((diameter/2.0 + 1.0) * self.zoom_factor))  # Increased from 0.5 to 1.0
+                # Use exact radius without any additions
+                zoomed_radius = int((diameter/2.0) * self.zoom_factor)
                 
                 # Draw main circle
                 cv2.circle(zoomed_bgr,